Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations13200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 MiB
Average record size in memory319.4 B

Variable types

Numeric8
Categorical4

Alerts

Atmospheric Pressure is highly overall correlated with TemperatureHigh correlation
Humidity is highly overall correlated with Precipitation (%) and 1 other fieldsHigh correlation
Precipitation (%) is highly overall correlated with Humidity and 2 other fieldsHigh correlation
Temperature is highly overall correlated with Atmospheric Pressure and 1 other fieldsHigh correlation
UV Index is highly overall correlated with Weather TypeHigh correlation
Visibility (km) is highly overall correlated with Humidity and 1 other fieldsHigh correlation
Weather Type is highly overall correlated with Precipitation (%) and 2 other fieldsHigh correlation
CaseId is uniformly distributed Uniform
Weather Type is uniformly distributed Uniform
CaseId has unique values Unique
Temperature has 206 (1.6%) zeros Zeros
Wind Speed has 170 (1.3%) zeros Zeros
UV Index has 2097 (15.9%) zeros Zeros

Reproduction

Analysis started2025-03-30 21:37:39.173249
Analysis finished2025-03-30 21:37:51.494092
Duration12.32 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

CaseId
Real number (ℝ)

Uniform  Unique 

Distinct13200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6600.5
Minimum1
Maximum13200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size103.2 KiB
2025-03-30T18:37:51.643873image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile660.95
Q13300.75
median6600.5
Q39900.25
95-th percentile12540.05
Maximum13200
Range13199
Interquartile range (IQR)6599.5

Descriptive statistics

Standard deviation3810.6561
Coefficient of variation (CV)0.5773284
Kurtosis-1.2
Mean6600.5
Median Absolute Deviation (MAD)3300
Skewness0
Sum87126600
Variance14521100
MonotonicityStrictly increasing
2025-03-30T18:37:51.850725image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
8805 1
 
< 0.1%
8795 1
 
< 0.1%
8796 1
 
< 0.1%
8797 1
 
< 0.1%
8798 1
 
< 0.1%
8799 1
 
< 0.1%
8800 1
 
< 0.1%
8801 1
 
< 0.1%
8802 1
 
< 0.1%
Other values (13190) 13190
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
13200 1
< 0.1%
13199 1
< 0.1%
13198 1
< 0.1%
13197 1
< 0.1%
13196 1
< 0.1%
13195 1
< 0.1%
13194 1
< 0.1%
13193 1
< 0.1%
13192 1
< 0.1%
13191 1
< 0.1%

Temperature
Real number (ℝ)

High correlation  Zeros 

Distinct126
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.127576
Minimum-25
Maximum109
Zeros206
Zeros (%)1.6%
Negative2337
Negative (%)17.7%
Memory size103.2 KiB
2025-03-30T18:37:52.056781image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-25
5-th percentile-9
Q14
median21
Q331
95-th percentile44
Maximum109
Range134
Interquartile range (IQR)27

Descriptive statistics

Standard deviation17.386327
Coefficient of variation (CV)0.90896655
Kurtosis0.58605063
Mean19.127576
Median Absolute Deviation (MAD)11
Skewness0.22174145
Sum252484
Variance302.28435
MonotonicityNot monotonic
2025-03-30T18:37:52.266261image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 391
 
3.0%
24 375
 
2.8%
26 372
 
2.8%
30 370
 
2.8%
22 365
 
2.8%
28 357
 
2.7%
27 355
 
2.7%
34 354
 
2.7%
31 347
 
2.6%
23 341
 
2.6%
Other values (116) 9573
72.5%
ValueCountFrequency (%)
-25 1
 
< 0.1%
-24 4
 
< 0.1%
-23 2
 
< 0.1%
-22 5
 
< 0.1%
-21 4
 
< 0.1%
-20 21
0.2%
-19 22
0.2%
-18 20
0.2%
-17 34
0.3%
-16 27
0.2%
ValueCountFrequency (%)
109 1
 
< 0.1%
108 1
 
< 0.1%
107 2
< 0.1%
102 1
 
< 0.1%
100 1
 
< 0.1%
99 2
< 0.1%
98 1
 
< 0.1%
97 3
< 0.1%
95 1
 
< 0.1%
94 2
< 0.1%

Humidity
Real number (ℝ)

High correlation 

Distinct90
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.710833
Minimum20
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size103.2 KiB
2025-03-30T18:37:52.466552image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile29
Q157
median70
Q384
95-th percentile98
Maximum109
Range89
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.194248
Coefficient of variation (CV)0.29390195
Kurtosis-0.33836557
Mean68.710833
Median Absolute Deviation (MAD)13
Skewness-0.40161427
Sum906983
Variance407.80766
MonotonicityNot monotonic
2025-03-30T18:37:52.673413image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 313
 
2.4%
60 309
 
2.3%
67 295
 
2.2%
63 291
 
2.2%
70 288
 
2.2%
69 284
 
2.2%
73 282
 
2.1%
68 282
 
2.1%
65 277
 
2.1%
78 276
 
2.1%
Other values (80) 10303
78.1%
ValueCountFrequency (%)
20 69
0.5%
21 65
0.5%
22 70
0.5%
23 71
0.5%
24 92
0.7%
25 67
0.5%
26 67
0.5%
27 59
0.4%
28 55
0.4%
29 63
0.5%
ValueCountFrequency (%)
109 42
0.3%
108 52
0.4%
107 46
0.3%
106 57
0.4%
105 50
0.4%
104 50
0.4%
103 41
0.3%
102 42
0.3%
101 36
0.3%
100 46
0.3%

Wind Speed
Real number (ℝ)

Zeros 

Distinct97
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.832197
Minimum0
Maximum48.5
Zeros170
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size103.2 KiB
2025-03-30T18:37:52.872931image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median9
Q313.5
95-th percentile20.5
Maximum48.5
Range48.5
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation6.9087041
Coefficient of variation (CV)0.70266128
Kurtosis3.2551937
Mean9.832197
Median Absolute Deviation (MAD)4.5
Skewness1.3602626
Sum129785
Variance47.730193
MonotonicityNot monotonic
2025-03-30T18:37:53.079242image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 470
 
3.6%
9.5 463
 
3.5%
9 456
 
3.5%
6.5 454
 
3.4%
6 436
 
3.3%
8.5 422
 
3.2%
7.5 421
 
3.2%
7 417
 
3.2%
5.5 413
 
3.1%
5 399
 
3.0%
Other values (87) 8849
67.0%
ValueCountFrequency (%)
0 170
1.3%
0.5 324
2.5%
1 319
2.4%
1.5 337
2.6%
2 326
2.5%
2.5 366
2.8%
3 347
2.6%
3.5 334
2.5%
4 316
2.4%
4.5 318
2.4%
ValueCountFrequency (%)
48.5 1
 
< 0.1%
47.5 1
 
< 0.1%
47 3
< 0.1%
46.5 3
< 0.1%
46 1
 
< 0.1%
45.5 1
 
< 0.1%
45 4
< 0.1%
44.5 3
< 0.1%
44 4
< 0.1%
43.5 4
< 0.1%

Precipitation (%)
Real number (ℝ)

High correlation 

Distinct110
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.644394
Minimum0
Maximum109
Zeros120
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size103.2 KiB
2025-03-30T18:37:53.270619image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q119
median58
Q382
95-th percentile98
Maximum109
Range109
Interquartile range (IQR)63

Descriptive statistics

Standard deviation31.946541
Coefficient of variation (CV)0.59552431
Kurtosis-1.3540386
Mean53.644394
Median Absolute Deviation (MAD)29
Skewness-0.15245707
Sum708106
Variance1020.5815
MonotonicityNot monotonic
2025-03-30T18:37:53.469566image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 222
 
1.7%
14 213
 
1.6%
16 211
 
1.6%
18 206
 
1.6%
17 203
 
1.5%
13 202
 
1.5%
15 200
 
1.5%
11 196
 
1.5%
12 187
 
1.4%
92 185
 
1.4%
Other values (100) 11175
84.7%
ValueCountFrequency (%)
0 120
0.9%
1 127
1.0%
2 109
0.8%
3 139
1.1%
4 120
0.9%
5 161
1.2%
6 133
1.0%
7 115
0.9%
8 125
0.9%
9 134
1.0%
ValueCountFrequency (%)
109 49
0.4%
108 47
0.4%
107 42
0.3%
106 38
0.3%
105 37
0.3%
104 41
0.3%
103 46
0.3%
102 38
0.3%
101 54
0.4%
100 39
0.3%

Cloud Cover
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size853.2 KiB
overcast
6090 
partly cloudy
4560 
clear
2139 
cloudy
 
411

Length

Max length13
Median length8
Mean length9.1788636
Min length5

Characters and Unicode

Total characters121161
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpartly cloudy
2nd rowpartly cloudy
3rd rowclear
4th rowclear
5th rowovercast

Common Values

ValueCountFrequency (%)
overcast 6090
46.1%
partly cloudy 4560
34.5%
clear 2139
 
16.2%
cloudy 411
 
3.1%

Length

2025-03-30T18:37:53.670056image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T18:37:53.817162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
overcast 6090
34.3%
cloudy 4971
28.0%
partly 4560
25.7%
clear 2139
 
12.0%

Most occurring characters

ValueCountFrequency (%)
c 13200
10.9%
r 12789
10.6%
a 12789
10.6%
l 11670
9.6%
o 11061
9.1%
t 10650
8.8%
y 9531
7.9%
e 8229
6.8%
v 6090
 
5.0%
s 6090
 
5.0%
Other values (4) 19062
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121161
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 13200
10.9%
r 12789
10.6%
a 12789
10.6%
l 11670
9.6%
o 11061
9.1%
t 10650
8.8%
y 9531
7.9%
e 8229
6.8%
v 6090
 
5.0%
s 6090
 
5.0%
Other values (4) 19062
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121161
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 13200
10.9%
r 12789
10.6%
a 12789
10.6%
l 11670
9.6%
o 11061
9.1%
t 10650
8.8%
y 9531
7.9%
e 8229
6.8%
v 6090
 
5.0%
s 6090
 
5.0%
Other values (4) 19062
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121161
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 13200
10.9%
r 12789
10.6%
a 12789
10.6%
l 11670
9.6%
o 11061
9.1%
t 10650
8.8%
y 9531
7.9%
e 8229
6.8%
v 6090
 
5.0%
s 6090
 
5.0%
Other values (4) 19062
15.7%

Atmospheric Pressure
Real number (ℝ)

High correlation 

Distinct5456
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1005.8279
Minimum800.12
Maximum1199.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size103.2 KiB
2025-03-30T18:37:54.002025image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum800.12
5-th percentile980.84
Q1994.8
median1007.65
Q31016.7725
95-th percentile1029.04
Maximum1199.21
Range399.09
Interquartile range (IQR)21.9725

Descriptive statistics

Standard deviation37.199589
Coefficient of variation (CV)0.036984049
Kurtosis12.778071
Mean1005.8279
Median Absolute Deviation (MAD)10.62
Skewness-0.29389861
Sum13276928
Variance1383.8094
MonotonicityNot monotonic
2025-03-30T18:37:54.202512image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1017.97 11
 
0.1%
1017.92 11
 
0.1%
1015.11 11
 
0.1%
1016.31 11
 
0.1%
1019.47 11
 
0.1%
1013.57 11
 
0.1%
1019.88 10
 
0.1%
1013.68 10
 
0.1%
1018.91 10
 
0.1%
992.52 10
 
0.1%
Other values (5446) 13094
99.2%
ValueCountFrequency (%)
800.12 1
< 0.1%
800.23 1
< 0.1%
800.82 1
< 0.1%
800.83 1
< 0.1%
801.25 1
< 0.1%
802.47 1
< 0.1%
803.02 1
< 0.1%
803.08 1
< 0.1%
803.29 1
< 0.1%
803.48 1
< 0.1%
ValueCountFrequency (%)
1199.21 1
< 0.1%
1198.97 1
< 0.1%
1198.85 1
< 0.1%
1198.41 1
< 0.1%
1197.92 1
< 0.1%
1197.2 1
< 0.1%
1197.04 1
< 0.1%
1196.03 1
< 0.1%
1196 1
< 0.1%
1195.34 1
< 0.1%

UV Index
Real number (ℝ)

High correlation  Zeros 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0057576
Minimum0
Maximum14
Zeros2097
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size103.2 KiB
2025-03-30T18:37:54.371760image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile11
Maximum14
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.8566004
Coefficient of variation (CV)0.96276429
Kurtosis-0.36216602
Mean4.0057576
Median Absolute Deviation (MAD)2
Skewness0.90001018
Sum52876
Variance14.873366
MonotonicityNot monotonic
2025-03-30T18:37:54.534483image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 2837
21.5%
0 2097
15.9%
2 1465
11.1%
3 1432
10.8%
4 831
 
6.3%
5 591
 
4.5%
11 582
 
4.4%
10 577
 
4.4%
9 572
 
4.3%
7 543
 
4.1%
Other values (5) 1673
12.7%
ValueCountFrequency (%)
0 2097
15.9%
1 2837
21.5%
2 1465
11.1%
3 1432
10.8%
4 831
 
6.3%
5 591
 
4.5%
6 524
 
4.0%
7 543
 
4.1%
8 532
 
4.0%
9 572
 
4.3%
ValueCountFrequency (%)
14 210
 
1.6%
13 189
 
1.4%
12 218
 
1.7%
11 582
4.4%
10 577
4.4%
9 572
4.3%
8 532
4.0%
7 543
4.1%
6 524
4.0%
5 591
4.5%

Season
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size812.2 KiB
Winter
5610 
Spring
2598 
Autumn
2500 
Summer
2492 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters79200
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter
2nd rowSpring
3rd rowSpring
4th rowSpring
5th rowWinter

Common Values

ValueCountFrequency (%)
Winter 5610
42.5%
Spring 2598
19.7%
Autumn 2500
18.9%
Summer 2492
18.9%

Length

2025-03-30T18:37:54.703727image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T18:37:54.841719image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
winter 5610
42.5%
spring 2598
19.7%
autumn 2500
18.9%
summer 2492
18.9%

Most occurring characters

ValueCountFrequency (%)
n 10708
13.5%
r 10700
13.5%
i 8208
10.4%
t 8110
10.2%
e 8102
10.2%
u 7492
9.5%
m 7484
9.4%
W 5610
7.1%
S 5090
6.4%
p 2598
 
3.3%
Other values (2) 5098
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 10708
13.5%
r 10700
13.5%
i 8208
10.4%
t 8110
10.2%
e 8102
10.2%
u 7492
9.5%
m 7484
9.4%
W 5610
7.1%
S 5090
6.4%
p 2598
 
3.3%
Other values (2) 5098
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 10708
13.5%
r 10700
13.5%
i 8208
10.4%
t 8110
10.2%
e 8102
10.2%
u 7492
9.5%
m 7484
9.4%
W 5610
7.1%
S 5090
6.4%
p 2598
 
3.3%
Other values (2) 5098
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 10708
13.5%
r 10700
13.5%
i 8208
10.4%
t 8110
10.2%
e 8102
10.2%
u 7492
9.5%
m 7484
9.4%
W 5610
7.1%
S 5090
6.4%
p 2598
 
3.3%
Other values (2) 5098
6.4%

Visibility (km)
Real number (ℝ)

High correlation 

Distinct41
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4629167
Minimum0
Maximum20
Zeros25
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size103.2 KiB
2025-03-30T18:37:55.020128image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37.5
95-th percentile10.5
Maximum20
Range20
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.3714989
Coefficient of variation (CV)0.61716096
Kurtosis2.5172751
Mean5.4629167
Median Absolute Deviation (MAD)2.5
Skewness1.2332752
Sum72110.5
Variance11.367005
MonotonicityNot monotonic
2025-03-30T18:37:55.206869image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1.5 829
 
6.3%
2 805
 
6.1%
3 803
 
6.1%
4 784
 
5.9%
2.5 783
 
5.9%
3.5 778
 
5.9%
4.5 761
 
5.8%
5 720
 
5.5%
5.5 714
 
5.4%
8 686
 
5.2%
Other values (31) 5537
41.9%
ValueCountFrequency (%)
0 25
 
0.2%
0.5 143
 
1.1%
1 504
3.8%
1.5 829
6.3%
2 805
6.1%
2.5 783
5.9%
3 803
6.1%
3.5 778
5.9%
4 784
5.9%
4.5 761
5.8%
ValueCountFrequency (%)
20 10
 
0.1%
19.5 34
0.3%
19 37
0.3%
18.5 25
0.2%
18 32
0.2%
17.5 32
0.2%
17 31
0.2%
16.5 44
0.3%
16 37
0.3%
15.5 31
0.2%

Location
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size825.1 KiB
inland
4816 
mountain
4813 
coastal
3571 

Length

Max length8
Median length7
Mean length6.9997727
Min length6

Characters and Unicode

Total characters92397
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowinland
2nd rowinland
3rd rowmountain
4th rowcoastal
5th rowmountain

Common Values

ValueCountFrequency (%)
inland 4816
36.5%
mountain 4813
36.5%
coastal 3571
27.1%

Length

2025-03-30T18:37:55.542845image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T18:37:55.689960image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
inland 4816
36.5%
mountain 4813
36.5%
coastal 3571
27.1%

Most occurring characters

ValueCountFrequency (%)
n 19258
20.8%
a 16771
18.2%
i 9629
10.4%
l 8387
9.1%
o 8384
9.1%
t 8384
9.1%
d 4816
 
5.2%
m 4813
 
5.2%
u 4813
 
5.2%
c 3571
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 92397
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 19258
20.8%
a 16771
18.2%
i 9629
10.4%
l 8387
9.1%
o 8384
9.1%
t 8384
9.1%
d 4816
 
5.2%
m 4813
 
5.2%
u 4813
 
5.2%
c 3571
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 92397
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 19258
20.8%
a 16771
18.2%
i 9629
10.4%
l 8387
9.1%
o 8384
9.1%
t 8384
9.1%
d 4816
 
5.2%
m 4813
 
5.2%
u 4813
 
5.2%
c 3571
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 92397
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 19258
20.8%
a 16771
18.2%
i 9629
10.4%
l 8387
9.1%
o 8384
9.1%
t 8384
9.1%
d 4816
 
5.2%
m 4813
 
5.2%
u 4813
 
5.2%
c 3571
 
3.9%

Weather Type
Categorical

High correlation  Uniform 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size802.6 KiB
Rainy
3300 
Cloudy
3300 
Sunny
3300 
Snowy
3300 

Length

Max length6
Median length5
Mean length5.25
Min length5

Characters and Unicode

Total characters69300
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRainy
2nd rowCloudy
3rd rowSunny
4th rowSunny
5th rowRainy

Common Values

ValueCountFrequency (%)
Rainy 3300
25.0%
Cloudy 3300
25.0%
Sunny 3300
25.0%
Snowy 3300
25.0%

Length

2025-03-30T18:37:55.921687image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T18:37:56.090932image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
rainy 3300
25.0%
cloudy 3300
25.0%
sunny 3300
25.0%
snowy 3300
25.0%

Most occurring characters

ValueCountFrequency (%)
n 13200
19.0%
y 13200
19.0%
o 6600
9.5%
u 6600
9.5%
S 6600
9.5%
R 3300
 
4.8%
a 3300
 
4.8%
i 3300
 
4.8%
C 3300
 
4.8%
l 3300
 
4.8%
Other values (2) 6600
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 13200
19.0%
y 13200
19.0%
o 6600
9.5%
u 6600
9.5%
S 6600
9.5%
R 3300
 
4.8%
a 3300
 
4.8%
i 3300
 
4.8%
C 3300
 
4.8%
l 3300
 
4.8%
Other values (2) 6600
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 13200
19.0%
y 13200
19.0%
o 6600
9.5%
u 6600
9.5%
S 6600
9.5%
R 3300
 
4.8%
a 3300
 
4.8%
i 3300
 
4.8%
C 3300
 
4.8%
l 3300
 
4.8%
Other values (2) 6600
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 13200
19.0%
y 13200
19.0%
o 6600
9.5%
u 6600
9.5%
S 6600
9.5%
R 3300
 
4.8%
a 3300
 
4.8%
i 3300
 
4.8%
C 3300
 
4.8%
l 3300
 
4.8%
Other values (2) 6600
9.5%

Interactions

2025-03-30T18:37:49.464492image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:40.053894image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:41.238865image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:42.492785image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:43.590090image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:44.733528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:46.158782image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:47.823366image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:49.745742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:40.209087image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:41.375704image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:42.630111image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:43.729493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:44.876600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:46.379500image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:48.041149image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:49.927045image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:40.351591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:41.517216image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:42.765851image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:43.867604image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:45.021023image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:46.556878image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:48.262537image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:50.284668image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:40.495292image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:41.646752image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:42.888801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:44.001641image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:45.151180image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:46.756628image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:48.425250image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:50.430421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:40.638754image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:41.910694image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:43.017882image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:44.135378image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:45.346016image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:46.974701image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:48.591174image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:50.560323image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:40.772357image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:42.041904image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:43.150566image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:44.278996image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:45.491449image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:47.197039image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:48.807609image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:50.733905image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:40.934617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:42.196838image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:43.301443image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:44.436296image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:45.708883image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:47.406727image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:49.039413image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:50.886396image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:41.080708image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:42.340877image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:43.441021image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:44.581480image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:45.924999image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:47.608558image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-30T18:37:49.260122image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-03-30T18:37:56.260602image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Atmospheric PressureCaseIdCloud CoverHumidityLocationPrecipitation (%)SeasonTemperatureUV IndexVisibility (km)Weather TypeWind Speed
Atmospheric Pressure1.0000.0000.388-0.3780.175-0.4480.2880.5430.4730.4490.464-0.228
CaseId0.0001.0000.0040.0140.0000.0080.0000.0060.008-0.0010.0150.016
Cloud Cover0.3880.0041.0000.2990.0850.3890.1440.2830.3880.3450.4870.194
Humidity-0.3780.0140.2991.0000.0760.6430.138-0.271-0.372-0.5370.3980.407
Location0.1750.0000.0850.0761.0000.0910.1430.1950.1240.0960.2160.030
Precipitation (%)-0.4480.0080.3890.6430.0911.0000.162-0.331-0.350-0.5620.5660.431
Season0.2880.0000.1440.1380.1430.1621.0000.3060.2160.1760.3440.055
Temperature0.5430.0060.283-0.2710.195-0.3310.3061.0000.4600.3640.509-0.128
UV Index0.4730.0080.388-0.3720.124-0.3500.2160.4601.0000.4410.526-0.180
Visibility (km)0.449-0.0010.345-0.5370.096-0.5620.1760.3640.4411.0000.451-0.362
Weather Type0.4640.0150.4870.3980.2160.5660.3440.5090.5260.4511.0000.280
Wind Speed-0.2280.0160.1940.4070.0300.4310.055-0.128-0.180-0.3620.2801.000

Missing values

2025-03-30T18:37:51.101811image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-30T18:37:51.365525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CaseIdTemperatureHumidityWind SpeedPrecipitation (%)Cloud CoverAtmospheric PressureUV IndexSeasonVisibility (km)LocationWeather Type
0114739.582partly cloudy1010.822Winter3.5inlandRainy
1239968.571partly cloudy1011.437Spring10.0inlandCloudy
2330647.016clear1018.725Spring5.5mountainSunny
3438831.582clear1026.257Spring1.0coastalSunny
45277417.066overcast990.671Winter2.5mountainRainy
5632553.526overcast1010.032Summer5.0inlandCloudy
67-2978.086overcast990.871Winter4.0inlandSnowy
783856.096partly cloudy984.461Winter3.5inlandSnowy
893836.066overcast999.440Winter1.0mountainSnowy
91028748.5107clear1012.138Winter7.5coastalSunny
CaseIdTemperatureHumidityWind SpeedPrecipitation (%)Cloud CoverAtmospheric PressureUV IndexSeasonVisibility (km)LocationWeather Type
131901319130243.516partly cloudy1017.5411Summer6.5mountainSunny
131911319227486.514clear1029.378Summer8.0inlandSunny
131921319331248.05clear1029.618Summer9.0inlandSunny
1319313194-56515.550overcast982.571Winter5.0inlandSnowy
1319413195296213.017overcast1002.812Spring5.0coastalCloudy
1319513196107414.571overcast1003.151Summer1.0mountainRainy
1319613197-1763.523cloudy1067.231Winter6.0coastalSnowy
131971319830775.528overcast1012.693Autumn9.0coastalCloudy
131981319937610.094overcast984.270Winter2.0inlandSnowy
1319913200-5380.092overcast1015.375Autumn10.0mountainRainy